There have been many attempts at image recognition systems, particularly directed towards recognizing written and printed characters. Optical character recognition systems, which recognize printed alphanumeric characters based upon expected character features and/or expected character size are well known. Recognition of handwritten characters, has proven to be more difficult.
Prior art systems include the use of matrix matching, neural networks, chaining, as well as conventional feature extraction. In a typical image recognition system, a library of reference images is first generated and stored, then each input image, or test image, is compared to the images stored in the library until a match is found. The difficulty lies in the amount of memory needed to store a library of all expected images, as well as the amount of processing time and power needed to detect a match. Handwritten characters are especially difficult because of variations in size, orientation and general distortions. Not only are the resulting prior art systems expensive, memory intensive and slow, but often fail to detect a match.